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   <div id="projectbrief">Reranking framework for structure prediction and discriminative language modeling</div>
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<a href="perceptron-model-proto-writer_8_c.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001 <span class="comment">// Copyright 2012, Google Inc.</span>
<a name="l00002"></a>00002 <span class="comment">// All rights reserved.</span>
<a name="l00003"></a>00003 <span class="comment">// </span>
<a name="l00004"></a>00004 <span class="comment">// Redistribution and use in source and binary forms, with or without</span>
<a name="l00005"></a>00005 <span class="comment">// modification, are permitted provided that the following conditions are</span>
<a name="l00006"></a>00006 <span class="comment">// met:</span>
<a name="l00007"></a>00007 <span class="comment">// </span>
<a name="l00008"></a>00008 <span class="comment">//   * Redistributions of source code must retain the above copyright</span>
<a name="l00009"></a>00009 <span class="comment">//     notice, this list of conditions and the following disclaimer.</span>
<a name="l00010"></a>00010 <span class="comment">//   * Redistributions in binary form must reproduce the above</span>
<a name="l00011"></a>00011 <span class="comment">//     copyright notice, this list of conditions and the following disclaimer</span>
<a name="l00012"></a>00012 <span class="comment">//     in the documentation and/or other materials provided with the</span>
<a name="l00013"></a>00013 <span class="comment">//     distribution.</span>
<a name="l00014"></a>00014 <span class="comment">//   * Neither the name of Google Inc. nor the names of its</span>
<a name="l00015"></a>00015 <span class="comment">//     contributors may be used to endorse or promote products derived from</span>
<a name="l00016"></a>00016 <span class="comment">//     this software without specific prior written permission.</span>
<a name="l00017"></a>00017 <span class="comment">//</span>
<a name="l00018"></a>00018 <span class="comment">// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS</span>
<a name="l00019"></a>00019 <span class="comment">// &quot;AS IS&quot; AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT</span>
<a name="l00020"></a>00020 <span class="comment">// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR</span>
<a name="l00021"></a>00021 <span class="comment">// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT</span>
<a name="l00022"></a>00022 <span class="comment">// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,</span>
<a name="l00023"></a>00023 <span class="comment">// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT</span>
<a name="l00024"></a>00024 <span class="comment">// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,           </span>
<a name="l00025"></a>00025 <span class="comment">// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY           </span>
<a name="l00026"></a>00026 <span class="comment">// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT</span>
<a name="l00027"></a>00027 <span class="comment">// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE</span>
<a name="l00028"></a>00028 <span class="comment">// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</span>
<a name="l00029"></a>00029 <span class="comment">// -----------------------------------------------------------------------------</span>
<a name="l00030"></a>00030 <span class="comment">//</span>
<a name="l00031"></a>00031 <span class="comment">//</span>
<a name="l00036"></a>00036 <span class="comment"></span>
<a name="l00037"></a>00037 <span class="preprocessor">#include &lt;sstream&gt;</span>
<a name="l00038"></a>00038 
<a name="l00039"></a>00039 <span class="preprocessor">#include &quot;../proto/dataio.h&quot;</span>
<a name="l00040"></a>00040 <span class="preprocessor">#include &quot;<a class="code" href="perceptron-model-proto-writer_8_h.html" title="Serializer for reranker::PerceptronModel instances to ModelMessage instances.">perceptron-model-proto-writer.H</a>&quot;</span>
<a name="l00041"></a>00041 
<a name="l00042"></a>00042 <span class="keyword">namespace </span>reranker {
<a name="l00043"></a>00043 
<a name="l00044"></a><a class="code" href="namespacereranker.html#a89f654b1eb630c8e3bd7e5bbce06c429">00044</a> <a class="code" href="model-proto-writer_8_h.html#aefedcce8169d9e150f15a4c54b373998" title="Registers the ModelProtoWriter  implementation with the specified subtype TYPE with the ModelProtoWri...">REGISTER_MODEL_PROTO_WRITER</a>(<a class="code" href="classreranker_1_1_perceptron_model_proto_writer.html" title="A class to construct a ModelMessage from a PerceptronModel instance.">PerceptronModelProtoWriter</a>)
<a name="l00045"></a>00045 
<a name="l00046"></a>00046 using confusion_learning::FeatureMessage;
<a name="l00047"></a>00047 using confusion_learning::FeatureVecMessage;
<a name="l00048"></a>00048 using confusion_learning::SymbolTableMessage;
<a name="l00049"></a>00049 using confusion_learning::SymbolMessage;
<a name="l00050"></a>00050 
<a name="l00051"></a>00051 <span class="keywordtype">void</span>
<a name="l00052"></a><a class="code" href="classreranker_1_1_perceptron_model_proto_writer.html#aa0f99ad5ab482cdc7b94d1a093379b59">00052</a> <a class="code" href="classreranker_1_1_perceptron_model_proto_writer.html" title="A class to construct a ModelMessage from a PerceptronModel instance.">PerceptronModelProtoWriter</a>::Write(const <a class="code" href="classreranker_1_1_model.html" title="Model is an interface for reranking models.">Model</a> *model,
<a name="l00053"></a>00053                                   ModelMessage *model_message,
<a name="l00054"></a>00054                                   <span class="keywordtype">bool</span> write_features)<span class="keyword"> const </span>{
<a name="l00055"></a>00055   <span class="keyword">const</span> <a class="code" href="classreranker_1_1_perceptron_model.html" title="This class implements a perceptron model reranker.">PerceptronModel</a> *perceptron_model =
<a name="l00056"></a>00056       <span class="keyword">dynamic_cast&lt;</span><span class="keyword">const </span><a class="code" href="classreranker_1_1_perceptron_model.html" title="This class implements a perceptron model reranker.">PerceptronModel</a> *<span class="keyword">&gt;</span>(model);
<a name="l00057"></a>00057   model_message-&gt;set_identifier(perceptron_model-&gt;<a class="code" href="classreranker_1_1_model.html#a7360b45e4a024759cd393b376209cf3c" title="Returns the unique name for this model instance.">name</a>());
<a name="l00058"></a>00058   model_message-&gt;set_reader_spec(perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#ac91dd3431ca9a536b7e31db72d052207">proto_reader_spec</a>());
<a name="l00059"></a>00059   model_message-&gt;set_num_iterations(perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#afae11bd9b41855ae74cf974fdf1ef417" title="Returns the epoch of the best models seen so far during training.">best_model_epoch</a>());
<a name="l00060"></a>00060   model_message-&gt;set_training_errors(perceptron_model-&gt;<a class="code" href="classreranker_1_1_model.html#a46c143623d0f7b51040927aa0fd9197b" title="Returns the number of training errors made by this model.">num_training_errors</a>());
<a name="l00061"></a>00061   model_message-&gt;set_model_spec(perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a80c863f01106f5721971324d8f23bc76">model_spec</a>());
<a name="l00062"></a>00062 
<a name="l00063"></a>00063 
<a name="l00064"></a>00064   <span class="keywordflow">if</span> (write_features) {
<a name="l00065"></a>00065     <span class="comment">// TODO(dbikel): Figure out exactly what quantity to serialize as</span>
<a name="l00066"></a>00066     <span class="comment">// model loss.</span>
<a name="l00067"></a>00067     FeatureVecMessage *raw_feature_vector_message =
<a name="l00068"></a>00068         model_message-&gt;mutable_raw_parameters();
<a name="l00069"></a>00069     fv_writer_.Write(perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a234283287d5a7f59fd8a1d1cf767fbc1" title="The best models seen so far during training, according to evaluation on the held-out development test...">best_models_</a>.<a class="code" href="classreranker_1_1_training_vector_set.html#a49b3808a509cf2e46edfdc4df6fbd62d" title="Returns the &quot;raw&quot; feature weights computed during training.">weights</a>(),
<a name="l00070"></a>00070                      FeatureMessage::BASIC,
<a name="l00071"></a>00071                      raw_feature_vector_message);
<a name="l00072"></a>00072     FeatureVecMessage *avg_feature_vector_message =
<a name="l00073"></a>00073         model_message-&gt;mutable_avg_parameters();
<a name="l00074"></a>00074     fv_writer_.Write(perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a234283287d5a7f59fd8a1d1cf767fbc1" title="The best models seen so far during training, according to evaluation on the held-out development test...">best_models_</a>.<a class="code" href="classreranker_1_1_training_vector_set.html#a21e449676837c2a0aa03a6749bc9b6f7" title="Returns the feature vector corresponding to the averaged perceptron.">average_weights</a>(),
<a name="l00075"></a>00075                      FeatureMessage::BASIC,
<a name="l00076"></a>00076                      avg_feature_vector_message);
<a name="l00077"></a>00077 
<a name="l00078"></a>00078     <span class="comment">// Don&#39;t write the symbol table if we are not writing the features.</span>
<a name="l00079"></a>00079     <span class="keywordflow">if</span> (perceptron_model-&gt;<a class="code" href="classreranker_1_1_model.html#a8c0f904234605692035ba179f85c53e2" title="The symbol table for this model (may be NULL).">symbols_</a> != NULL) {
<a name="l00080"></a>00080       SymbolTableMessage *symbol_table_message =
<a name="l00081"></a>00081           model_message-&gt;mutable_symbols();
<a name="l00082"></a>00082       <a class="code" href="classreranker_1_1_symbols.html" title="An interface specifying a converter from symbols (strings) to int indices.">Symbols</a> *symbols = perceptron_model-&gt;<a class="code" href="classreranker_1_1_model.html#a8c0f904234605692035ba179f85c53e2" title="The symbol table for this model (may be NULL).">symbols_</a>;
<a name="l00083"></a>00083       <span class="keywordflow">for</span> (<a class="code" href="classreranker_1_1_symbols.html#ac2f04202711c3f61038ca43656edeb92">Symbols::const_iterator</a> it = symbols-&gt;<a class="code" href="classreranker_1_1_symbols.html#ae63ad8cbeb0ab390a50e9f8b8deaa998">begin</a>();
<a name="l00084"></a>00084            it != symbols-&gt;<a class="code" href="classreranker_1_1_symbols.html#a299004b5ead84217cc11ce43dd41b359">end</a>();
<a name="l00085"></a>00085            ++it) {
<a name="l00086"></a>00086         SymbolMessage *symbol_message = symbol_table_message-&gt;add_symbol();
<a name="l00087"></a>00087         symbol_message-&gt;set_symbol(it-&gt;first);
<a name="l00088"></a>00088         symbol_message-&gt;set_index(it-&gt;second);
<a name="l00089"></a>00089       }
<a name="l00090"></a>00090     }
<a name="l00091"></a>00091   }
<a name="l00092"></a>00092 }
<a name="l00093"></a>00093 
<a name="l00094"></a>00094 <span class="keywordtype">void</span>
<a name="l00095"></a><a class="code" href="classreranker_1_1_perceptron_model_proto_writer.html#a4368ea895c4b5e3fbbc38610d98d9d36">00095</a> <a class="code" href="classreranker_1_1_perceptron_model_proto_writer.html#a4368ea895c4b5e3fbbc38610d98d9d36" title="Writes out the features of this model to a series of FeatureMessage instances using the specified Con...">PerceptronModelProtoWriter::WriteFeatures</a>(<span class="keyword">const</span> <a class="code" href="classreranker_1_1_model.html" title="Model is an interface for reranking models.">Model</a> *model,
<a name="l00096"></a>00096                                           ostream &amp;os,
<a name="l00097"></a>00097                                           <span class="keywordtype">bool</span> output_best_epoch,
<a name="l00098"></a>00098                                           <span class="keywordtype">double</span> weight,
<a name="l00099"></a>00099                                           <span class="keywordtype">bool</span> output_key,
<a name="l00100"></a>00100                                           <span class="keyword">const</span> <span class="keywordtype">string</span> separator)<span class="keyword"></span>
<a name="l00101"></a>00101 <span class="keyword">    const </span>{
<a name="l00102"></a>00102   ConfusionProtoIO proto_writer;
<a name="l00103"></a>00103   <span class="keyword">const</span> <a class="code" href="classreranker_1_1_perceptron_model.html" title="This class implements a perceptron model reranker.">PerceptronModel</a> *perceptron_model =
<a name="l00104"></a>00104       <span class="keyword">dynamic_cast&lt;</span><span class="keyword">const </span><a class="code" href="classreranker_1_1_perceptron_model.html" title="This class implements a perceptron model reranker.">PerceptronModel</a> *<span class="keyword">&gt;</span>(model);
<a name="l00105"></a>00105   <span class="keyword">const</span> <a class="code" href="classreranker_1_1_feature_vector.html">FeatureVector&lt;int, double&gt;</a> &amp;avg_weights =
<a name="l00106"></a>00106       output_best_epoch ?
<a name="l00107"></a>00107       perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a234283287d5a7f59fd8a1d1cf767fbc1" title="The best models seen so far during training, according to evaluation on the held-out development test...">best_models_</a>.<a class="code" href="classreranker_1_1_training_vector_set.html#a21e449676837c2a0aa03a6749bc9b6f7" title="Returns the feature vector corresponding to the averaged perceptron.">average_weights</a>() :
<a name="l00108"></a>00108       perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a6f913688d02fe624d91d1416c71e157c" title="The feature vectors representing this model.">models_</a>.<a class="code" href="classreranker_1_1_training_vector_set.html#a21e449676837c2a0aa03a6749bc9b6f7" title="Returns the feature vector corresponding to the averaged perceptron.">average_weights</a>();
<a name="l00109"></a>00109   <span class="keyword">const</span> <a class="code" href="classreranker_1_1_feature_vector.html">FeatureVector&lt;int, double&gt;</a> &amp;raw_weights =
<a name="l00110"></a>00110       perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a6f913688d02fe624d91d1416c71e157c" title="The feature vectors representing this model.">models_</a>.<a class="code" href="classreranker_1_1_training_vector_set.html#a49b3808a509cf2e46edfdc4df6fbd62d" title="Returns the &quot;raw&quot; feature weights computed during training.">weights</a>();
<a name="l00111"></a>00111   FeatureMessage feature_message;
<a name="l00112"></a>00112   <span class="keywordflow">for</span> (<a class="code" href="classreranker_1_1_feature_vector.html" title="A class to represent a feature vector, where features are represented by unique identifiers, and feature values are represented by the template type.">FeatureVector&lt;int, double&gt;::const_iterator</a> it = raw_weights.<a class="code" href="classreranker_1_1_feature_vector.html#ae0c185e646996daff25fcad9b224eda5" title="Returns a const iterator pointing to the first of the feature-value pairs of this feature vector...">begin</a>();
<a name="l00113"></a>00113        it != raw_weights.<a class="code" href="classreranker_1_1_feature_vector.html#a9f2539faf4e600c2f75b613763d49fcd" title="Returns a const iterator pointing to the end of the feature-value pairs of this feature vector...">end</a>();
<a name="l00114"></a>00114        ++it) {
<a name="l00115"></a>00115     feature_message.Clear();
<a name="l00116"></a>00116     <a class="code" href="classreranker_1_1_symbols.html" title="An interface specifying a converter from symbols (strings) to int indices.">Symbols</a> *symbols = perceptron_model-&gt;<a class="code" href="classreranker_1_1_model.html#a736bec0b8f468285580982df181a7cd0" title="Returns the symbol table for this model.">symbols</a>();
<a name="l00117"></a>00117     fv_writer_.<a class="code" href="classreranker_1_1_feature_vector_writer.html#a10d99f419685418695e72c427971714e" title="Serializes a single feature-value pair to a FeatureMessage.">SerializeFeature</a>(it-&gt;first, weight * it-&gt;second,
<a name="l00118"></a>00118                                 FeatureMessage::BASIC,
<a name="l00119"></a>00119                                 &amp;feature_message,
<a name="l00120"></a>00120                                 symbols);
<a name="l00121"></a>00121     <span class="comment">// Lookup the average weight.</span>
<a name="l00122"></a>00122     <span class="keywordflow">if</span> (!avg_weights.<a class="code" href="classreranker_1_1_feature_vector.html#a72b41f295541c52d9ad4a25e789b7ba4">empty</a>()) {
<a name="l00123"></a>00123       feature_message.set_avg_value(weight * avg_weights.<a class="code" href="classreranker_1_1_feature_vector.html#a565133f9b5b31de43d07aea909631be5" title="Synonymous with GetWeight.">GetValue</a>(it-&gt;first));
<a name="l00124"></a>00124     }
<a name="l00125"></a>00125     <span class="keywordtype">string</span> encoded_message;
<a name="l00126"></a>00126     proto_writer.EncodeBase64(feature_message, &amp;encoded_message);
<a name="l00127"></a>00127     <span class="keywordflow">if</span> (output_key) {
<a name="l00128"></a>00128       <span class="keyword">const</span> <span class="keywordtype">string</span> &amp;feat_name = symbols-&gt;<a class="code" href="classreranker_1_1_symbols.html#aa1f272dec203cba98c2c5bbb128af3b1" title="Returns the unique symbol for the specified index, or the empty string if the specified index does no...">GetSymbol</a>(it-&gt;first);
<a name="l00129"></a>00129       <span class="keywordflow">if</span> (feat_name == <span class="stringliteral">&quot;&quot;</span>) {
<a name="l00130"></a>00130         stringstream ss;
<a name="l00131"></a>00131         ss &lt;&lt; it-&gt;first;
<a name="l00132"></a>00132         os &lt;&lt; ss.str() &lt;&lt; separator;
<a name="l00133"></a>00133       } <span class="keywordflow">else</span> {
<a name="l00134"></a>00134         os &lt;&lt; feat_name &lt;&lt; separator;
<a name="l00135"></a>00135       }
<a name="l00136"></a>00136     }
<a name="l00137"></a>00137     os &lt;&lt; encoded_message;
<a name="l00138"></a>00138   }
<a name="l00139"></a>00139   os.flush();
<a name="l00140"></a>00140 }
<a name="l00141"></a>00141 
<a name="l00142"></a>00142 }  <span class="comment">// namespace reranker</span>
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